def test_msd_global_temp(self):
"""Tests diffusion via MSD for global gamma and temperature"""
gamma = 9.4
kT = 0.37
dt = 0.5
system = self.system
system.part.clear()
p = system.part.add(pos=(0, 0, 0), id=0)
system.time_step = dt
system.thermostat.set_brownian(kT=kT, gamma=gamma, seed=42)
system.cell_system.skin = 0.4
pos_obs = ParticlePositions(ids=(p.id, ))
c_pos = Correlator(obs1=pos_obs,
tau_lin=16,
tau_max=100.,
delta_N=10,
corr_operation="square_distance_componentwise",
compress1="discard1")
system.auto_update_accumulators.add(c_pos)
system.integrator.run(500000)
c_pos.finalize()
# Check MSD
msd = c_pos.result()
system.auto_update_accumulators.clear()
def expected_msd(x):
return 2. * kT / gamma * x
for i in range(2, 6):
np.testing.assert_allclose(msd[i, 2:5],
expected_msd(msd[i, 0]),
rtol=0.02)
compress1="discard1")
c_vel = Correlator(obs1=vel_obs,
tau_lin=16,
tau_max=20.,
delta_N=1,
corr_operation="scalar_product",
compress1="discard1")
system.auto_update_accumulators.add(c_pos)
system.auto_update_accumulators.add(c_vel)
system.integrator.run(1000000)
c_pos.finalize()
c_vel.finalize()
np.savetxt("msd.dat", c_pos.result())
np.savetxt("vacf.dat", c_vel.result())
# Integral of vacf via Green-Kubo
# D= 1/3 int_0^infty <v(t_0)v(t_0+t)> dt
vacf = c_vel.result()
# Integrate with trapezoidal rule
I = np.trapz(vacf[:, 2], vacf[:, 0])
ratio = 1. / 3. * I / (kT / gamma)
print("Ratio of measured and expected diffusion coefficients from Green-Kubo:",
ratio)
# Check MSD
msd = c_pos.result()
# correlator
ang_id = ParticleAngularVelocities(ids=[0])
avacf = Correlator(obs1=ang_id,
corr_operation="scalar_product",
delta_N=1,
tau_max=tmax,
tau_lin=16)
system.auto_update_accumulators.add(avacf)
# Integrate 5,000,000 steps. This can be done in one go as well.
for i in range(SAMP_STEPS):
if (i + 1) % 100 == 0:
print('\rrun %i: %.0f%%' % (run + 1, (i + 1) * 100. / SAMP_STEPS),
end='',
flush=True)
system.integrator.run(SAMP_LENGTH)
print()
# Finalize the accumulators and write to disk
system.auto_update_accumulators.remove(msd)
msd.finalize()
np.savetxt("{}/msd_{}_{}.dat".format(outdir, vel, run), msd.result())
system.auto_update_accumulators.remove(vacf)
vacf.finalize()
np.savetxt("{}/vacf_{}_{}.dat".format(outdir, vel, run), vacf.result())
system.auto_update_accumulators.remove(avacf)
avacf.finalize()
np.savetxt("{}/avacf_{}_{}.dat".format(outdir, vel, run), avacf.result())
def calc(var):
# AVB: Create an output directory for this to store the output files
outdir = "./Noelle/r01.5kBT4Ads/1000=3.2"
if not os.path.exists(outdir):
os.makedirs(outdir)
# Setup constant
time_step = 0.01
loops = 30
step_per_loop = 100
# AVB: the parameters (that I usually use)
a = 0.05
r0 = 2.0 * a
kBT = 4.0e-6
vwf_type = 0
collagen_type = 1
monomer_mass = 0.01
box_l = 32.0
#print("Shear velocity:")
#shear_velocity = float(input())
#vy = box_l*shear_velocity
vy = var
print(vy)
v = [0, vy, 0]
# System setup
system = 0
system = System(box_l=[box_l, box_l, box_l])
system.set_random_state_PRNG()
np.random.seed(seed=system.seed)
system.cell_system.skin = 0.4
mpc = 20 # The number of monomers has been set to be 20 as default
# Change this value for further simulations
# Fene interaction
fene = interactions.FeneBond(k=0.04, d_r_max=0.3)
system.bonded_inter.add(fene)
# Setup polymer of part_id 0 with fene bond
# AVB: Notice the mode, max_tries and shield parameters for pruned self-avoiding random walk algorithm
polymer.create_polymer(N_P=1,
MPC=mpc,
bond=fene,
bond_length=r0,
start_pos=[29.8, 16.0, 16.0],
mode=2,
max_tries=100,
shield=0.6 * r0)
# AVB: setting the type of particles and changing mass of each monomer to 0.01
system.part[:].type = vwf_type
system.part[:].mass = monomer_mass
# AVB: I suggest to add Lennard-Jones interaction between the monomers
# AVB: to reproduce hydrophobicity
# AVB: parameters for the potential (amplitude and cut-off redius)
amplVwfVwf = 4.0 * kBT # sometimes we change this to 2.0*kBT
rcutVwfVwf = 1.5 * r0
# AVB: the potential
system.non_bonded_inter[vwf_type, vwf_type].lennard_jones.set_params(
epsilon=amplVwfVwf,
sigma=r0 / 1.122,
shift="auto",
cutoff=rcutVwfVwf,
min=r0 * 0.6)
print("Warming up the polymer chain.")
## For longer chains (>100) an extensive
## warmup is neccessary ...
system.time_step = 0.002
system.thermostat.set_langevin(kT=4.0e-6, gamma=1.0)
# AVB: Here the Langevin thermostat is needed, because we have not yet initialized the LB-fluid.
# AVB: And somehow it is necessary so that the polymer adopts the equilibrium conformation of the globule.
# AVB: you may skip this step
for i in range(100):
system.force_cap = float(i) + 1
system.integrator.run(100)
print("Warmup finished.")
system.force_cap = 0
system.integrator.run(100)
system.time_step = time_step
system.integrator.run(500)
# AVB: the following command turns the Langevin thermostat on in line 49
system.thermostat.turn_off()
# AVB: This command sets the velocities of all particles to zero
system.part[:].v = [0, 0, 0]
# AVB: The density was too small here. I have set 1.0 for now.
# AVB: It would be necessary to recalculate, but the density of the liquid should not affect the movements of the polymer (this is how our physical model works).
lbf = espressomd.lb.LBFluid(agrid=1,
dens=1.0,
visc=1.0e2,
tau=time_step,
fric=0.01)
system.actors.add(lbf)
system.thermostat.set_lb(kT=4.0e-6)
# Setup boundaries
walls = [lbboundaries.LBBoundary() for k in range(2)]
walls[0].set_params(shape=shapes.Wall(normal=[1, 0, 0], dist=1.5),
velocity=v)
walls[1].set_params(shape=shapes.Wall(normal=[-1, 0, 0], dist=-30.5))
for wall in walls:
system.lbboundaries.add(wall)
print("Warming up the system with LB fluid.")
system.integrator.run(5000)
print("LB fluid warming finished.")
# AVB: after this you should have a completely collapsed polymer globule
# AVB: If you want to watch the process of globule formation in Paraview, just change 5000 to 0 in line 100
N = 25
x_coord = np.array([30] * N)
y_coord = np.arange(14, 24, 5 / N)
z_coord = np.arange(14, 24, 5 / N)
for i in range(N):
for j in range(N):
system.part.add(id=i * N + j + 100,
pos=np.array([x_coord[i], y_coord[j], z_coord[i]]),
v=np.array([0, 0, 0]),
type=i * N + j + 100)
all_collagen = range(100, (N - 1) * N + (N - 1) + 100)
system.comfixed.types = all_collagen
for i in range(100, (N - 1) * N + (N - 1) + 100):
system.non_bonded_inter[vwf_type,
i].lennard_jones.set_params(epsilon=amplVwfVwf,
sigma=r0 / 1.122,
shift="auto",
cutoff=rcutVwfVwf,
min=r0 * 0.6)
# configure correlators
com_pos = ComPosition(ids=(0, ))
c = Correlator(obs1=com_pos,
tau_lin=16,
tau_max=loops * step_per_loop,
delta_N=1,
corr_operation="square_distance_componentwise",
compress1="discard1")
system.auto_update_accumulators.add(c)
print("Sampling started.")
print("lenth after warmup")
print(
system.analysis.calc_re(chain_start=0,
number_of_chains=1,
chain_length=mpc - 1)[0])
lengths = []
ylengths = []
for i in range(loops):
system.integrator.run(step_per_loop)
system.analysis.append()
lengths.append(
system.analysis.calc_re(chain_start=0,
number_of_chains=1,
chain_length=mpc - 1)[0])
lbf.print_vtk_velocity(outdir + "/" + str(vy) + "%04i.vtk" % i)
system.part.writevtk(outdir + "/" + str(vy) + "vwf_all%04i.vtk" % i,
types=all_collagen)
system.part.writevtk(outdir + "/" + str(vy) + "vwf_poly%04i.vtk" % i,
types=[0])
cor = list(system.part[:].pos)
y = []
for l in cor:
y.append(l[1])
ylengths.append(max(y) - min(y))
sys.stdout.write("\rSampling: %05i" % i)
sys.stdout.flush()
walls[0].set_params(shape=shapes.Wall(normal=[1, 0, 0], dist=1.5))
walls[1].set_params(shape=shapes.Wall(normal=[-1, 0, 0], dist=-30.5))
for i in range(100):
system.integrator.run(step_per_loop)
lengths.append(
system.analysis.calc_re(chain_start=0,
number_of_chains=1,
chain_length=mpc - 1)[0])
system.part.writevtk(outdir + "/" + str(vy) +
"vwf_all[r0=2,kBT=4]intheEND.vtk")
with open(outdir + "/lengths" + str(vy) + ".dat", "a") as datafile:
datafile.write("\n".join(map(str, lengths)))
with open(outdir + "/lengthsY" + str(vy) + ".dat", "a") as datafile:
datafile.write("\n".join(map(str, ylengths)))
mean_vy = [(vy * 10000) / 32, sum(ylengths) / len(ylengths)]
print("mean_vy")
print(mean_vy)
with open(outdir + "/mean_vy" + "2kBT_2r0" + ".dat", "a") as datafile:
datafile.write(" ".join(map(str, mean_vy)))
c.finalize()
corrdata = c.result()
corr = zeros((corrdata.shape[0], 2))
corr[:, 0] = corrdata[:, 0]
corr[:, 1] = (corrdata[:, 2] + corrdata[:, 3] + corrdata[:, 4]) / 3
savetxt(outdir + "/msd_nom" + str(mpc) + ".dat", corr)
with open(outdir + "/rh_out.dat", "a") as datafile:
rh = system.analysis.calc_rh(chain_start=0,
number_of_chains=1,
chain_length=mpc - 1)
datafile.write(str(mpc) + " " + str(rh[0]) + "\n")
tau_lin=16)
system.auto_update_accumulators.add(msd)
## Exercise 3 ##
# Construct the auto-accumulators for the VACF and AVACF,
# using the example of the MSD
# Initialize the velocity auto-correlation function (VACF) correlator
...
# Initialize the angular velocity auto-correlation function (AVACF)
# correlator
...
# Integrate 5,000,000 steps. This can be done in one go as well.
for i in range(sampsteps):
system.integrator.run(samplength)
# Finalize the accumulators and write to disk
system.auto_update_accumulators.remove(msd)
msd.finalize()
np.savetxt("{}/msd_{}_{}.dat".format(outdir, vel, run), msd.result())
...
...
pos_id = ParticlePositions(ids=[cent])
msd = Correlator(obs1=pos_id,
corr_operation="square_distance_componentwise",
delta_N=1,
tau_max=tmax,
tau_lin=16)
system.auto_update_accumulators.add(msd)
## Exercise 7a ##
# Construct the auto-correlators for the AVACF, using the example
# of the MSD.
...
# Perform production
# Integrate
for k in range(prod_steps):
print("Production {} of 5: {} of {}".format(cnt + 1, k, prod_steps))
system.integrator.run(prod_length)
# Finalize the MSD and export
system.auto_update_accumulators.remove(msd)
msd.finalize()
np.savetxt("{}/msd_{}.dat".format(outdir, cnt), msd.result())
## Exercise 7b ##
# Finalize the angular velocity auto-correlation function (AVACF)
# correlator and write the result to a file.
...
# Integrate 5,000,000 steps. This can be done in one go as well.
for i in range(SAMP_STEPS):
if (i + 1) % 100 == 0:
print('\rrun %i: %.0f%%' % (run + 1, (i + 1) * 100. / SAMP_STEPS),
end='',
flush=True)
system.integrator.run(SAMP_LENGTH)
print()
# Finalize the accumulators and write to disk
system.auto_update_accumulators.remove(msd)
msd.finalize()
np.savetxt(
"{}/msd_{}_{}.dat".format(outdir, vel, run),
np.column_stack(
(msd.lag_times(), msd.sample_sizes(), msd.result().reshape([-1,
3]))))
system.auto_update_accumulators.remove(vacf)
vacf.finalize()
np.savetxt(
"{}/vacf_{}_{}.dat".format(outdir, vel, run),
np.column_stack((vacf.lag_times(), vacf.sample_sizes(),
vacf.result().reshape([-1, 1]))))
system.auto_update_accumulators.remove(avacf)
avacf.finalize()
np.savetxt(
"{}/avacf_{}_{}.dat".format(outdir, vel, run),
np.column_stack((avacf.lag_times(), avacf.sample_sizes(),
avacf.result().reshape([-1, 1]))))
kin_stress.write(str(stress['kinetic'][0, 1]) + "\n")
kin_stress.flush()
temp.write(str(energy['kinetic']) + "\n")
temp.flush()
ener.write(str(energy['total']) + "\n")
ener.flush()
print(i, flush=True)
system.integrator.run(iterations)
# Finalizing and results of correlators
c_dpd.finalize()
c_old.finalize()
dpd_stress_acf = c_dpd.result()
old_stress_acf = c_old.result()
#Saving the results of correlators in numpy-files
np.save('dpd_sample_dpd_stress_acf.npy', dpd_stress_acf)
np.save('dpd_sample_old_stress_acf.npy', old_stress_acf)
# Close non-correlated stress files
dpd_stress.close()
old_stress.close()
kin_stress.close()
temp.close()
ener.close()
c_vel = Correlator(obs1=vel_obs,
tau_lin=16,
tau_max=20.,
delta_N=1,
corr_operation="scalar_product",
compress1="discard1")
system.auto_update_accumulators.add(c_pos)
system.auto_update_accumulators.add(c_vel)
system.integrator.run(1000000)
c_pos.finalize()
c_vel.finalize()
msd = np.column_stack(
(c_pos.lag_times(), c_pos.sample_sizes(), c_pos.result().reshape([-1, 3])))
vacf = np.column_stack(
(c_vel.lag_times(), c_vel.sample_sizes(), c_vel.result().reshape([-1, 1])))
np.savetxt("msd.dat", msd)
np.savetxt("vacf.dat", vacf)
# Integral of vacf via Green-Kubo
# D= 1/3 int_0^infty <v(t_0)v(t_0+t)> dt
# Integrate with trapezoidal rule
I = np.trapz(vacf[:, 2], vacf[:, 0])
ratio = 1. / 3. * I / (kT / gamma)
print("Ratio of measured and expected diffusion coefficients from Green-Kubo:",
ratio)
for pt in system.part.select(lambda p: True):
force = (pt.f[0]**2 + pt.f[1]**2 + pt.f[2]**2)**(1 / 2)
if force > max:
max = force
print(max)
from espressomd import electrostatics
p3m = electrostatics.P3M(prefactor=1.0, accuracy=1e-2)
system.actors.add(p3m)
checkpoint.register("p3m")
fp.close()
# Finalize the correlator and obtain the results
msd_corr.finalize()
msd = msd_corr.result()
# STEP 6
import matplotlib.pyplot as plt
fig1 = plt.figure(num=None,
figsize=(10, 6),
dpi=80,
facecolor='w',
edgecolor='k')
fig1.set_tight_layout(False)
plt.plot(r00, avg_rdf00, '-', color="#A60628", linewidth=2, alpha=1)
plt.plot(r11, avg_rdf11, '-', color="#1528b5", linewidth=2, alpha=1)
plt.plot(r01, avg_rdf01, '-', color="#0dbf22", linewidth=2, alpha=1)
plt.xlabel('$r$', fontsize=20)
plt.ylabel('$g(r)$', fontsize=20)
c = Correlator(obs1=com_pos,
tau_lin=16,
tau_max=loops * step_per_loop,
delta_N=1,
corr_operation="square_distance_componentwise",
compress1="discard1")
system.auto_update_accumulators.add(c)
print("Sampling started.")
for i in range(loops):
system.integrator.run(step_per_loop)
system.analysis.append()
lbf.print_vtk_velocity(outdir + "/fluid%04i.vtk" % i)
system.part.writevtk(outdir + "/vwf_all%04i.vtk" % i)
sys.stdout.write("\rSampling: %05i" % i)
sys.stdout.flush()
c.finalize()
corrdata = c.result()
corr = zeros((corrdata.shape[0], 2))
corr[:, 0] = corrdata[:, 0]
corr[:, 1] = (corrdata[:, 2] + corrdata[:, 3] + corrdata[:, 4]) / 3
savetxt(outdir + "/msd_nom" + str(mpc) + ".dat", corr)
with open(outdir + "/rh_out.dat", "a") as datafile:
rh = system.analysis.calc_rh(chain_start=0,
number_of_chains=1,
chain_length=mpc - 1)
datafile.write(str(mpc) + " " + str(rh[0]) + "\n")
# Open h5md file
h5 = h5md.H5md(filename="./trajectory.h5", write_pos=True, write_lees_edwards_offset = True, write_vel = True, write_ordered=True)
for i in range(args.samples):
print(i, flush=True)
h5.write()
system.integrator.run(5000)
#Close all the files
h5.close()
# Finalizing and results of correlators
c_pos.finalize()
c_vel.finalize()
vacf = c_vel.result()
msd = c_pos.result()
#Saving the results of correlators in numpy-files
np.save('dpd_sample_msd.npy', msd)
np.save('dpd_sample_vacf.npy', vacf)
# Integral of vacf via Green-Kubo
# D= 1/3 int_0^infty <v(t_0)v(t_0+t)> dt
I = 1. / 3. * integrate.cumtrapz(vacf[:, 2], vacf[:, 0], initial=0) / n_part
popt1, pcov1 = curve_fit(vacf_fit, vacf[:,0], I, sigma = 1./np.sqrt(vacf[:, 1]))
# Fit MSD to analytical solution
# <x(t_0)x(t_0+t)> = 6 * D * t
popt2, pcov2 = curve_fit(msd_fit, msd[:, 0], msd[:, 2]+msd[:, 3]+msd[:, 4], sigma = 1./np.sqrt(msd[:, 1]))